【作者】 孟庆丰；

【导师】 焦李成；

【作者基本信息】 西安电子科技大学 ， 模式识别与智能系统， 2006， 博士

【摘要】 信号特征提取是从信号中获取信息的过程,是模式识别、智能系统和机械故障诊断等诸多领域的基础和关键。动态信号的复杂性和特征提取的多学科交叉融合特性使得信号特征提取方法一直是人们广为关注的重要研究方向。本文以把握学术前沿为前提,以多学科知识的相互渗透和紧密结合工程应用为主要思路,探讨信号特征提取方法的研究途径,研究具有工程实用意义的信号特征提取方法。主要研究工作如下:1.研究了离散Fourier变换中存在的谱泄漏效应对多频信号的频率、幅值和相位估计精度的影响。结合插值快速Fourier变换,并考虑了长程泄漏的影响,提出了一种迭代插值FFT方法。方法可有效地消除长程泄漏效应,提高估值精度。与著名的Grandke的IFFT和Liguori的IFFTc方法的比较结果表明,在信号存在噪声干扰以及信号参数不同的情况下,该方法获得的结果有最好的估计精度,有最好的综合性能。2.分析了机械系统动态响应的波形特点,提出用指数衰减正弦波作为字典原子来分解信号,并结合匹配追踪和遗传算法给出了一种自适应信号分解方法。方法能有效地提取平稳的周期波形和非平稳的冲击衰减响应。与著名的Gabor原子的比较表明,该方法对提取机械冲击响应比用Gabor字典原子更有效,提取的结果有更为明确的物理解释,可以获得更为稀疏的信号表示。方法已成功应用于往复机械的故障诊断。3.研究了基于非参数波形原子的特征波形提取方法。将具有先验知识的模板信号通过滤波器组得到一组不需要用任何参数表达的基函数,由此构造出非参数特征波形原子。模板信号的引入使得提取的特征波形具有物理可解释的特点;应用滤波器组形成的特征波形原子具备了形状和位置可调的能力,因此具有很宽的适应性。仿真和实验信号验证了方法的有效性,尤其是在噪声和信号频带重叠的情况下,也能将信号分离和提取出来。4.研究了由微弱的随机激励引起的瞬态波形的提取方法。从理论上证明了周期干扰成分的存在对提取的随机减量特征信号的影响,结合数字滤波技术和随机减量技术,提出了一种从周期干扰环境中提取随机减量特征信号的简便方法。用该方法实现了旋转机械油膜涡动的在线监测和稳定性裕度的趋势分析与预测,解决了这一应用难题,获得了满意的结果。5.针对感应电动机系统无传感器监测与诊断中存在的特征信息微弱、易受环境噪更多还原

【Abstract】 Signal feature extraction is a process that obtains information from signals and a foundational and key technique for many fields such as pattern recognition, intelligent system and machinery fault diagnosis. Feature extraction method has been a research direction followed with interest duo to the complexity of signals and the combinability of multidisciplinary knowledge for feature extraction. Following the latest progress of signal processing techniques, this dissertation explores ways of studying feature extraction methods and develops feature extraction methods by closely combining multidisciplinary knowledge with specific engineering applications. The research work is introduced as follows:1.Effect of spectral leakage in discrete Fourier transform on frequency, amplitude and phase estimates of multifrequency signals is studied. Based on the interpolated fast Fourier transform (IFFT), and considering the long-range leakage effect, an iterative IFFT algorithm is proposed. The novel method can eliminate the long-range leakage effect and improve the accuracy of the parameter estimates effectively. A comparative study of the proposed method with the well-known Grandke’s IFFT and Liguori’s IFFTc is presented. It is found that, in the noise circumstance and with the diverse frequency, amplitude and phase parameters, the proposed method outperforms the other ones, and provides the best estimates.2.Exponentially decayed sinusoidal function is suggested as a dictionary atom after having a good understanding of machinery impulse response waveforms. A method of adaptive signal representation with the atom is proposed based on the matching pursuit and the genetic algorithms. By using the proposed method, both the periodic and the impulse response waveforms can be separately extracted from the signal. Experimental results of machinery dynamic signal decompositions show that the proposed method can efficiently yield representation which is sparser and physically more interpretable than using the well-known Gabor atom.3.A nonparametric method for extracting feature waveform from signal is studied. Using template signal that contains prior information, a set of nonparametric basis functions is obtained firstly by means of a filter bank, and then a feature waveform atom that is described without any parameters is constructed. The feature waveforms extracted from signal using the method is physically interpretable duo to the employ of template signal. The filter bank makes the dictionary atom shape adaptive. Simulated and experimental results更多还原